{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GAN Workflow Engine with the MedNIST Dataset\n",
    "\n",
    "The MONAI framework can be used to easily design, train, and evaluate generative adversarial networks.\n",
    "This notebook exemplifies using MONAI components to design and train a simple GAN model to reconstruct images of Hand CT scans.\n",
    "\n",
    "Read the [MONAI Mednist GAN Tutorial](https://github.com/Project-MONAI/tutorials/blob/master/modules/mednist_GAN_tutorial.ipynb) for details about the network architecture and loss functions.\n",
    "\n",
    "**Table of Contents**\n",
    "\n",
    "1. Setup\n",
    "2. Initialize MONAI Components\n",
    "    * Create image transform chain\n",
    "    * Create dataset and dataloader\n",
    "    * Define generator and discriminator\n",
    "    * Create training handlers\n",
    "    * Create GanTrainer\n",
    "3. Run Training\n",
    "4. Evaluate Results\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/master/modules/mednist_GAN_workflow_array.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Setup\n",
    "\n",
    "### Setup environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python -c \"import monai\" || pip install -q \"monai-weekly[ignite, tqdm]\"\n",
    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from monai.utils import set_determinism\n",
    "from monai.transforms import (\n",
    "    AddChannel,\n",
    "    Compose,\n",
    "    RandFlip,\n",
    "    RandRotate,\n",
    "    RandZoom,\n",
    "    ScaleIntensity,\n",
    "    EnsureType,\n",
    "    Transform,\n",
    ")\n",
    "from monai.networks.nets import Discriminator, Generator\n",
    "from monai.networks import normal_init\n",
    "from monai.handlers import CheckpointSaver, MetricLogger, StatsHandler\n",
    "from monai.engines.utils import GanKeys, default_make_latent\n",
    "from monai.engines import GanTrainer\n",
    "from monai.data import CacheDataset, DataLoader\n",
    "from monai.config import print_config\n",
    "from monai.apps import download_and_extract\n",
    "import numpy as np\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import shutil\n",
    "import sys\n",
    "import logging\n",
    "import tempfile\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MONAI version: 0.6.0rc1+23.gc6793fd0\n",
      "Numpy version: 1.20.3\n",
      "Pytorch version: 1.9.0a0+c3d40fd\n",
      "MONAI flags: HAS_EXT = True, USE_COMPILED = False\n",
      "MONAI rev id: c6793fd0f316a448778d0047664aaf8c1895fe1c\n",
      "\n",
      "Optional dependencies:\n",
      "Pytorch Ignite version: 0.4.5\n",
      "Nibabel version: 3.2.1\n",
      "scikit-image version: 0.15.0\n",
      "Pillow version: 7.0.0\n",
      "Tensorboard version: 2.5.0\n",
      "gdown version: 3.13.0\n",
      "TorchVision version: 0.10.0a0\n",
      "ITK version: 5.1.2\n",
      "tqdm version: 4.53.0\n",
      "lmdb version: 1.2.1\n",
      "psutil version: 5.8.0\n",
      "pandas version: 1.1.4\n",
      "einops version: 0.3.0\n",
      "\n",
      "For details about installing the optional dependencies, please visit:\n",
      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup data directory\n",
    "\n",
    "You can specify a directory with the `MONAI_DATA_DIRECTORY` environment variable.  \n",
    "This allows you to save results and reuse downloads.  \n",
    "If not specified a temporary directory will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/workspace/data/medical\n"
     ]
    }
   ],
   "source": [
    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
    "print(root_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download dataset\n",
    "\n",
    "Downloads and extracts the dataset.\n",
    "\n",
    "The MedNIST dataset was gathered from several sets from [TCIA](https://wiki.cancerimagingarchive.net/display/Public/Data+Usage+Policies+and+Restrictions),\n",
    "[the RSNA Bone Age Challenge](http://rsnachallenges.cloudapp.net/competitions/4),\n",
    "and [the NIH Chest X-ray dataset](https://cloud.google.com/healthcare/docs/resources/public-datasets/nih-chest).\n",
    "\n",
    "The dataset is kindly made available by [Dr. Bradley J. Erickson M.D., Ph.D.](https://www.mayo.edu/research/labs/radiology-informatics/overview) (Department of Radiology, Mayo Clinic)\n",
    "under the Creative Commons [CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/).\n",
    "\n",
    "\n",
    "If you use the MedNIST dataset, please acknowledge the source, e.g.  \n",
    "https://github.com/Project-MONAI/tutorials/blob/master/2d_classification/mednist_tutorial.ipynb."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "resource = \"https://drive.google.com/uc?id=1QsnnkvZyJPcbRoV_ArW8SnE1OTuoVbKE\"\n",
    "md5 = \"0bc7306e7427e00ad1c5526a6677552d\"\n",
    "\n",
    "compressed_file = os.path.join(root_dir, \"MedNIST.tar.gz\")\n",
    "data_dir = os.path.join(root_dir, \"MedNIST\")\n",
    "if not os.path.exists(data_dir):\n",
    "    download_and_extract(resource, compressed_file, root_dir, md5)\n",
    "\n",
    "hands = [\n",
    "    os.path.join(data_dir, \"Hand\", x)\n",
    "    for x in os.listdir(os.path.join(data_dir, \"Hand\"))\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/workspace/data/medical/MedNIST/Hand/000317.jpeg', '/workspace/data/medical/MedNIST/Hand/002344.jpeg', '/workspace/data/medical/MedNIST/Hand/000816.jpeg', '/workspace/data/medical/MedNIST/Hand/004046.jpeg', '/workspace/data/medical/MedNIST/Hand/003316.jpeg']\n"
     ]
    }
   ],
   "source": [
    "print(hands[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Initialize MONAI components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
    "set_determinism(0)\n",
    "device = torch.device(\"cuda:0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create image transform chain\n",
    "\n",
    "Define the processing pipeline to convert saved disk images into usable Tensors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LoadTarJpeg(Transform):\n",
    "    def __call__(self, data):\n",
    "        return plt.imread(data)\n",
    "\n",
    "\n",
    "train_transforms = Compose(\n",
    "    [\n",
    "        LoadTarJpeg(),\n",
    "        AddChannel(),\n",
    "        ScaleIntensity(),\n",
    "        RandRotate(range_x=np.pi / 12, prob=0.5, keep_size=True),\n",
    "        RandFlip(spatial_axis=0, prob=0.5),\n",
    "        RandZoom(min_zoom=0.9, max_zoom=1.1, prob=0.5),\n",
    "        EnsureType(),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create dataset and dataloader\n",
    "\n",
    "Hold data and present batches during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:09<00:00, 1092.83it/s]\n"
     ]
    }
   ],
   "source": [
    "real_dataset = CacheDataset(hands, train_transforms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 300\n",
    "real_dataloader = DataLoader(\n",
    "    real_dataset, batch_size=batch_size, shuffle=True, num_workers=10)\n",
    "\n",
    "# We don't need to do any preparing so just return \"as is\"\n",
    "\n",
    "\n",
    "def prepare_batch(batchdata, device=None, non_blocking=False):\n",
    "    return batchdata.to(device=device, non_blocking=non_blocking)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define generator and discriminator\n",
    "\n",
    "Load basic computer vision GAN networks from libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define networks\n",
    "disc_net = Discriminator(\n",
    "    in_shape=(1, 64, 64),\n",
    "    channels=(8, 16, 32, 64, 1),\n",
    "    strides=(2, 2, 2, 2, 1),\n",
    "    num_res_units=1,\n",
    "    kernel_size=5,\n",
    ").to(device)\n",
    "\n",
    "latent_size = 64\n",
    "gen_net = Generator(\n",
    "    latent_shape=latent_size,\n",
    "    start_shape=(latent_size, 8, 8),\n",
    "    channels=[32, 16, 8, 1],\n",
    "    strides=[2, 2, 2, 1],\n",
    ")\n",
    "gen_net.conv.add_module(\"activation\", torch.nn.Sigmoid())\n",
    "gen_net = gen_net.to(device)\n",
    "\n",
    "# initialize both networks\n",
    "disc_net.apply(normal_init)\n",
    "gen_net.apply(normal_init)\n",
    "\n",
    "# define optimizors\n",
    "learning_rate = 2e-4\n",
    "betas = (0.5, 0.999)\n",
    "disc_opt = torch.optim.Adam(disc_net.parameters(), learning_rate, betas=betas)\n",
    "gen_opt = torch.optim.Adam(gen_net.parameters(), learning_rate, betas=betas)\n",
    "\n",
    "# define loss functions\n",
    "disc_loss_criterion = torch.nn.BCELoss()\n",
    "gen_loss_criterion = torch.nn.BCELoss()\n",
    "real_label = 1\n",
    "fake_label = 0\n",
    "\n",
    "\n",
    "def discriminator_loss(gen_images, real_images):\n",
    "    real = real_images.new_full((real_images.shape[0], 1), real_label)\n",
    "    gen = gen_images.new_full((gen_images.shape[0], 1), fake_label)\n",
    "\n",
    "    realloss = disc_loss_criterion(disc_net(real_images), real)\n",
    "    genloss = disc_loss_criterion(disc_net(gen_images.detach()), gen)\n",
    "\n",
    "    return (genloss + realloss) / 2\n",
    "\n",
    "\n",
    "def generator_loss(gen_images):\n",
    "    output = disc_net(gen_images)\n",
    "    cats = output.new_full(output.shape, real_label)\n",
    "    return gen_loss_criterion(output, cats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create training handlers\n",
    "\n",
    "Perform operations during model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "metric_logger = MetricLogger(\n",
    "    loss_transform=lambda x: {\n",
    "        GanKeys.GLOSS: x[GanKeys.GLOSS], GanKeys.DLOSS: x[GanKeys.DLOSS]},\n",
    "    metric_transform=lambda x: x,\n",
    ")\n",
    "\n",
    "handlers = [\n",
    "    StatsHandler(\n",
    "        name=\"batch_training_loss\",\n",
    "        output_transform=lambda x: {\n",
    "            GanKeys.GLOSS: x[GanKeys.GLOSS],\n",
    "            GanKeys.DLOSS: x[GanKeys.DLOSS],\n",
    "        },\n",
    "    ),\n",
    "    CheckpointSaver(\n",
    "        save_dir=os.path.join(root_dir, \"hand-gan\"),\n",
    "        save_dict={\"g_net\": gen_net, \"d_net\": disc_net},\n",
    "        save_interval=10,\n",
    "        save_final=True,\n",
    "        epoch_level=True,\n",
    "    ),\n",
    "    metric_logger,\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create GanTrainer\n",
    "\n",
    "MONAI Workflow engine for adversarial learning. The components come together here with the GanTrainer.\n",
    "\n",
    "Uses a training loop based on Goodfellow et al. 2014 https://arxiv.org/abs/1406.266.\n",
    "\n",
    "```\n",
    "Training Loop: for each batch of data size m\n",
    "        1. Generate m fakes from random latent codes.\n",
    "        2. Update D with these fakes and current batch reals, repeated d_train_steps times.\n",
    "        3. Generate m fakes from new random latent codes.\n",
    "        4. Update generator with these fakes using discriminator feedback.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "disc_train_steps = 5\n",
    "max_epochs = 50\n",
    "\n",
    "trainer = GanTrainer(\n",
    "    device,\n",
    "    max_epochs,\n",
    "    real_dataloader,\n",
    "    gen_net,\n",
    "    gen_opt,\n",
    "    generator_loss,\n",
    "    disc_net,\n",
    "    disc_opt,\n",
    "    discriminator_loss,\n",
    "    d_prepare_batch=prepare_batch,\n",
    "    d_train_steps=disc_train_steps,\n",
    "    g_update_latents=True,\n",
    "    latent_shape=latent_size,\n",
    "    key_train_metric=None,\n",
    "    train_handlers=handlers,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Start Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "is_executing": true
    },
    "scrolled": true,
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [],
   "source": [
    "trainer.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate Results\n",
    "\n",
    "Examine G and D loss curves for collapse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "g_loss = [loss[1][GanKeys.GLOSS] for loss in metric_logger.loss]\n",
    "d_loss = [loss[1][GanKeys.DLOSS] for loss in metric_logger.loss]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "is_executing": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "plt.semilogy(g_loss, label=\"Generator Loss\")\n",
    "plt.semilogy(d_loss, label=\"Discriminator Loss\")\n",
    "plt.grid(True, \"both\", \"both\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View image reconstructions\n",
    "With random latent codes view trained generator output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_img_count = 10\n",
    "test_latents = default_make_latent(test_img_count, latent_size).to(device)\n",
    "fakes = gen_net(test_latents)\n",
    "\n",
    "fig, axs = plt.subplots(2, (test_img_count // 2), figsize=(20, 8))\n",
    "axs = axs.flatten()\n",
    "for i, ax in enumerate(axs):\n",
    "    ax.axis(\"off\")\n",
    "    ax.imshow(fakes[i, 0].cpu().data.numpy(), cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleanup data directory\n",
    "\n",
    "Remove directory if a temporary was used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "if directory is None:\n",
    "    shutil.rmtree(root_dir)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
